Skip to main content
Glama

list_voices

Retrieve available text-to-speech voices with IDs, names, languages, and previews for voice selection in speech synthesis.

Instructions

List all available text-to-speech voices.

Returns voice IDs, names, supported languages, and sample previews.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that implements the list_voices tool. It makes an async HTTP GET request to /v1/tts/voices endpoint and returns the JSON response containing voice IDs, names, supported languages, and sample previews.
    async def list_voices() -> dict:
        """List all available text-to-speech voices.
    
        Returns voice IDs, names, supported languages, and sample previews.
        """
        async with _client() as client:
            response = await client.get("/v1/tts/voices")
            response.raise_for_status()
            return response.json()
  • server.py:118-118 (registration)
    Decorator that registers the list_voices function as an MCP tool with the FastMCP server.
    @mcp.tool()
  • Helper function that creates and configures an httpx.AsyncClient with the API base URL, authorization headers, and timeout settings. Used by the list_voices handler to make HTTP requests.
    def _client() -> httpx.AsyncClient:
        return httpx.AsyncClient(
            base_url=API_BASE,
            headers=_headers,
            timeout=60.0,
        )
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns voice IDs, names, languages, and sample previews, which adds useful context about output behavior. However, it lacks details on rate limits, authentication needs, or pagination, leaving gaps for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose and followed by return details. Every sentence adds value: the first defines the action, and the second specifies output content. There's no wasted text, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (0 parameters) and the presence of an output schema, the description is reasonably complete. It covers the purpose and output semantics, which is sufficient for a listing tool. However, without annotations, it could benefit from more behavioral context like rate limits or auth requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate. Baseline is 4 for 0 parameters, as it doesn't need to compensate for any schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'List all available text-to-speech voices.' It specifies the verb ('List') and resource ('text-to-speech voices'), making the action and target explicit. However, it doesn't differentiate from siblings like 'assess_pronunciation' or 'synthesize_speech' beyond implying it's a listing operation versus synthesis/assessment tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing voice IDs for synthesis, or contrast it with sibling tools like 'synthesize_speech' for generating speech. Usage is implied by the listing action, but no explicit context or exclusions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/fasuizu-br/brainiall-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server